The present invention relates to the field of oil and gas subsurface earth formation evaluation techniques and more particularly, to methods for selecting well candidate layers for stimulation treatments in a subterranean formation.
Oil and gas hydrocarbons may occupy pore spaces in subterranean formations such as, for example, in sandstone earth formations. The pore spaces are often interconnected and have a certain permeability, which is a measure of the ability of the rock to transmit fluid flow. Stimulation treatment operations such as, hydraulic fracturing or acid stimulation may be performed to increase the production from a wellbore if the near-wellbore permeability is low or when damage has occurred to the near-wellbore area.
Hydraulic fracturing is a process by which a fluid under high pressure is injected into the formation to create and/or enhance fractures that penetrate into the subterranean formation. These fractures can create flow channels to improve the near term productivity of the well. Propping agents of various kinds, chemical or physical, are often used to hold the fractures open and to prevent the healing of the fractures after the fracturing pressure is released.
Stimulation treatments may encounter a variety of problems during stimulation operations resulting in a less than optimal stimulation treatment. Accordingly, after a stimulation treatment, it may be desirable to evaluate the effectiveness of the stimulation treatment performed or to provide a baseline of reservoir properties for later comparison and evaluation. One example of a problem occasionally encountered in fracturing treatments for example is bypassed layers. That is, during an original completion, oil or gas wells may contain layers bypassed either intentionally or inadvertently. Additionally, over time, the effectiveness of stimulation treatments may decrease, resulting in portions of subterranean formations becoming less productive. Fines migration is an example of one way in which stimulation treatments can lose their effectiveness over time. Migration of fines may cause particles suspended in the produced fluid to bridge the pore throats near the wellbore so as to reduce well productivity.
When considering stimulation treatments or restimulation treatments for a number of wells or a number of subterranean layers in a well, it is desirable to choose the wells and/or layers to be treated in an economically optimal fashion. Often, it may not be economically feasible or advisable to perform stimulation treatments on all wells and/or on all layers of each well. Thus, operators will often attempt to select a subset of wells and/or layers to stimulate. Choosing the best candidates for stimulation treatments has been attempted by a variety of methods.
To select the best candidate for stimulation or restimulation, there are many parameters to be considered. Some important parameters for hydraulic fracturing may include, for example, formation permeability, in-situ stress distribution, reservoir fluid viscosity, skin factor, effective fracture half-length, fracture conductivity, and reservoir pressure. Various methods have been developed to estimate formation properties and thereby to use these estimated properties to evaluate the effectiveness of previous stimulation treatments. Once formation properties are estimated for given wells and/or layers, these formation properties may be used to select wells or layers to be stimulated. Additionally, these estimated formation properties may be used to develop stimulation treatments suited for the selected wells and/or layers.
Numerous methods have been developed to select well candidates for stimulation. One example of a conventional method for selecting stimulation candidates includes artificial neural network programs that can be “trained” with a set of input and output parameters. Training implies that the neural network develops a relationship between a given set of input and output parameters. After training, the neural network is used as a predictive tool to identify stimulation candidates. Virtual intelligence may be used by training an artificial neural network with production, completion and fracturing variables that include fracturing fluid type, breaker type, and breaker concentration, etc. After training, the artificial neural network may be used to identify stimulation candidate wells with relatively poor fracture treatment design or poor execution by comparing predicted and actual well performance.
Another method of identifying and selecting well candidates for stimulation uses production statistics or moving domain analysis implemented on a computer. This method compares production indicators of each well with its offsets to identify well underperformance. By comparing a well's production with only the immediate offset well production, the variability of reservoir quality may be minimized in the comparison.
Still another conventional method uses production type-curve analysis. Production type-curve analysis requires history matching well production using analytical type-curves developed specifically for single layer hydraulically fractured low permeability gas wells. Restimulation candidates may be identified by a short effective fracture half-length and the production increase potential of extending the effective fracture half-length with a restimulation treatment.
Unfortunately, these methods suffer a variety of drawbacks including the disadvantage that they evaluate the restimulation potential of each well and not individual layers in a multilevel completion. That is, these conventional methods evaluate well performance as opposed to identifying bypassed layers that could be at or near virgin reservoir pressure. Additionally, these methods may be suited to selecting and developing stimulation treatments for wells but lack cost-effective methods for selecting and developing stimulation treatment for individual layers in a multilayer completion. Additionally, conventional methods may lack the degree of accuracy desired for estimating reservoir properties that may be desired to select candidates for stimulation treatments.